Data is a business’s best friend. But it can also be a nightmare, especially if it is sub-standard. Fortunately, there are plenty of strategies that can be used to improve data quality and build solid data practice into the fabric of your business.
So, what is data quality? Data quality is the ability of a dataset to serve its purpose.
Put simply: If your data is low quality, it won’t be as helpful in what you want it to do with it. This can make the difference between keeping your head above water or sinking. Why? Because poor quality data leads to poor finances.
Data Quality Metrics - Part 1
These six simple criteria can help your company measure data quality. In an ideal world, they are all as important as one another. However, a lot depends on what you intend to use data for as this can influence what criteria you prioritise. All of the criteria are equal, but some are more equal than others.
1. Homogeneity
Homogeneity is vital. It means ensuring data can be compared and contrasted across different data sets. This can be achieved by universality and consistency. Keep it simple. Keep things clear.
2. Accuracy
Accuracy refers to whether your data is correct. Has it come from the source and can you prove that it hasn’t been changed? This question is yes, then you are well on your way to data accuracy.
3. Validity
Not to be confused with accuracy, validity can be used to assess whether your data is the type you wanted, without bias. To work this out, ask yourself: does data reflect what you want it to reflect? Is it complete, reasonable, and sound?
Data Quality Metrics - Part 2
4. Uniqueness
Uniqueness is knowing how to differentiate one from another. Sometimes we know we have a duplicate record; each record is unique. At the same time, we have identified a unique instance of a business concept that is recognised in the business glossary.
5. Opportuneness
Opportuneness or timeliness surrounds the date of your data. Older data is more likely to be less relevant to your business as it grows and adapts. Ensure data is updated and monitored regularly. Do not let it languish.
6. Completeness
The final, perhaps most fundamental, criteria asks if data is complete. Does a data set have everything it needs? Where are the holes and how can they be filled?
Whatever path you choose to improve the quality of your data, you must be sure that you also measure the effectiveness of your efforts. This will help you realise if that time and money is paying off.
Find out more about data quality in our Data Quality Course and learn how to implement practices in your business.
